1. Field of the Invention
The invention generally relates to telecommunications, and in particular to Service Rate allocation algorithms based upon the observed performance, a traffic learning process to improve the estimate of the expected traffic, and a mechanism for QoS provisioning measurement of Traffic Flows in a system.
2. Description of the Prior Art
Given the development of 3 G systems and the applications they are expected to support, the resources allocated to flow requests should be optimized to increase the capacity of the system but at the same time meet the QoS requirements of the flow. However, the airlink performance is of varying nature. The existing algorithms, which allocate some fixed amount of resources, for example resources allocated, can be minimum resources or maximum resources based. This allocation of resources does not consider the traffic nature. Resources are allocated to the flow at the time the flow is requested. If the resources are allocated based on the minimum requirements, QoS violations result (QoS performance of the flow is deteriorated). Maximum allocation leads to under-utilization of system resources.
Different applications have different QoS requirements. Typically the characteristics of a flow are given by the data-rate requirement and delay requirement. When assuming fixed traffic patterns the required service rate to be allocated may be computed based thereon. However, for the next generation applications it is not just sufficient to guarantee the average rate for the applications given the burstiness of the applications which are expected to be supported and the airlink performance as well.
Further, the requirements of the flow requests for complying with the specific requirements of the flow are not easy to estimate. Given the varied type of applications which users can run, it is not possible for the application (e.g. RAB parameters) to include all the possible aspects of the traffic. For example considering Variable Bit Rate (VBR) traffic requests, it is not possible to quantify the characteristics of the traffic. If the application is quantified based upon delay/jitter requirements or traffic burstiness, it is not possible to extract these parameters for different applications.
User flow requests usually have a QoS profile. The QoS profile information can be used to know the upper and the lower bound of the flow requests requirements. However, user flow requests do not know the parameters of the flow request. Instead of putting the burden on the user flow request, it is the system which tries to determine the service parameters that are required for this new flow request and based upon that, the resources allocated to this flow can be adjusted to meet the QoS requirements of the flows. Apart from the traffic characteristics of the user flow, the system also estimates the required resources for the current airlink performance. It is not possible for the user flow to know the radio conditions but if the system can maintain the history of the flow request and the airlink performance, then it is possible for the system to predict the requirements of the new flow request.
Given the bursty nature of the traffic and the varying airlink performance, it is not sufficient to just allocate resources based on the average data-rate and average delay requirements to meet the promised QoS. To meet the QoS and utilize resources efficiently, it is preferable to dynamically adjust the allocated resources to react to the traffic burstiness and/or air link performance. In order to do that, efficient mechanisms to measure the actual QoS would be needed.